Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Properties of measures of information in evidence and possibility theories
Fuzzy Sets and Systems
Handbook of defeasible reasoning and uncertainty management systems
IEEE Transactions on Knowledge and Data Engineering
Non-monotonic Syntax-Based Entailment: A Classification of Consequence Relations
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Quasi-classical Logic: Non-trivializable classical reasoning from incosistent information
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Measuring inconsistency in knowledge via quasi-classical models
Eighteenth national conference on Artificial intelligence
Quantifying information and contradiction in propositional logic through test actions
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evaluating significance of inconsistencies
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Possibility and necessity functions over non-classical logic
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Penalty logic and its link with Dempster-Shafer theory
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Adaptive Merging of Prioritized Knowledge Bases
Fundamenta Informaticae
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Possibilistic logic and quasi-classical logic are two logics that were developed in artificial intelligence for coping with inconsistency in different ways, yet preserving the main features of classical logic. This paper presents a new logic, called quasi-possibilistic logic, that encompasses possibilistic logic and quasi-classical logic, and preserves the merits of both logics. Indeed, it can handle plain conflicts taking place at the same level of certainty (as in quasi-classical logic), and take advantage of the stratification of the knowledge base into certainty layers for introducing gradedness in conflict analysis (as in possibilistic logic). When querying knowledge bases, it may be of interest to evaluate the extent to which the relevant available information is precise and consistent. The paper review measures of (im)precision and inconsistency/conflict existing in possibilistic logic and quasi-classical logic, and proposes generalized measures in the unified framework.